Bonmin¶

class Bonmin(*args)¶

Bonmin MINLP solver.

Bonmin is an open-source code for solving general MINLP problems.

Parameters:

problemOptimizationProblem, optional: Optimization problem to solve. Default is an empty problem.
algoNamestr, optional: Identifier of the optimization method to use. Default is ‘B-BB’

See also

AbdoRackwitz, Cobyla, Dlib, NLopt

Notes

Available algorithms:

Bonmin provides algorithms for the resolution of general optimization only. In particular, least squares problems or nearest point problems are not supported.

Bonmin provides the following algorithms:

Algorithm

Description

B-BB

NLP-based branch-and-bound

B-OA

Outer-approximation decomposition

B-QG

Quesada and Grossmann branch-and-cut

B-Hyb

Hybrid outer-approximation based branch-and-cut

B-Ecp

ECP cuts based branch-and-cut a la FilMINT

B-iFP

Iterated feasibility pump algorithm for MINLP

Algorithms parameters:

Bonmin algorithms can be adapted using numerous parameters, described here. These parameters can be modified using the ResourceMap. For every option optionName, one simply add a key named Bonmin-optionName with the value to use, as shown below:
>>> import openturns as ot
>>> ot.ResourceMap.AddAsUnsignedInteger('Bonmin-bonmin.iteration_limit', 100)
>>> ot.ResourceMap.AddAsScalar('Bonmin-bonmin.time_limit', 30.0)

Convergence criteria:

To estimate the convergence of the algorithm during the optimization process, Bonmin uses specific tolerance parameters, different from the standard absolute/relative/residual errors used in OpenTURNS. The definition of Bonmin’s parameters can be found in this paper, page 3.

Thus the attributes maximumAbsoluteError, maximumRelativeError, maximumResidualError and maximumConstraintError defined in OptimizationAlgorithm are not used in this case.

Examples

The code below ensures the optimization of the following problem:

$min \left( - x_0 - x_1 - x_2 \right)$

subject to

$\left(x_1 - \frac{1}{2}\right)^2 + \left(x_2 - \frac{1}{2}\right)^2 \leq \frac{1}{4} x_0 - x_1 \leq 0 x_0 + x_2 + x_3 \leq 2 x_0 \in \{0,1\}^n (x_1, x_2) \in \mathbb{R}^2 x_3 \in \mathbb{N}$

>>> import openturns as ot

>>> # Definition of objective function
>>> objectiveFunction = ot.SymbolicFunction(['x0','x1','x2','x3'], ['-x0 -x1 -x2'])

>>> # Definition of variables bounds
>>> bounds = ot.Interval([0,0,0,0],[1,1e308,1e308,5],[True,True,True,True],[True,False,False,True])

>>> # Definition of variables types
>>> variablesType = [ot.OptimizationProblemImplementation.BINARY,
... ot.OptimizationProblemImplementation.CONTINUOUS,
... ot.OptimizationProblemImplementation.CONTINUOUS,
... ot.OptimizationProblemImplementation.INTEGER]

In OpenTURNS, inequality constraints are defined by a function $h$ such that $h(x) \geq 0$ . The inequality expression above has to be modified to match this formulation.

>>> # Definition of constraints
>>> # Constraints in OpenTURNS are defined as g(x) = 0 and h(x) >= 0
>>> #    No equality constraint -> nothing to do
>>> #    Inequality constraints:
>>> h = ot.SymbolicFunction(['x0','x1','x2','x3'], ['-(x1-1/2)^2 - (x2-1/2)^2 + 1/4', '-x0 + x1', '-x0 - x2 - x3 + 2'])

>>> # Setting up Bonmin problem
>>> problem = ot.OptimizationProblem(objectiveFunction)
>>> problem.setBounds(bounds)
>>> problem.setVariablesType(variablesType)
>>> problem.setInequalityConstraint(h)

>>> bonminAlgorithm = ot.Bonmin(problem,'B-BB')
>>> bonminAlgorithm.setStartingPoint([0,0,0,0])
>>> bonminAlgorithm.setMaximumEvaluationNumber(10000)
>>> bonminAlgorithm.setMaximumIterationNumber(1000)
>>> ot.ResourceMap.AddAsString('Bonmin-mu_oracle','loqo')
>>> ot.ResourceMap.SetAsScalar('Bonmin-bonmin.time_limit', 5.0)

>>> # Running the solver
>>> bonminAlgorithm.run() 

>>> # Retrieving the results
>>> result = bonminAlgorithm.getResult() 
>>> optimalPoint = result.getOptimalPoint() 
>>> optimalValue = result.getOptimalValue() 
>>> evaluationNumber = result.getInputSample().getSize() 

Methods

`GetAlgorithmNames`()	Retrieves the names of the available optimization algorithms.
`getAlgorithmName`()	Accessor to algoName parameter.
`getClassName`()	Accessor to the object's name.
`getId`()	Accessor to the object's id.
`getMaximumAbsoluteError`()	Accessor to maximum allowed absolute error.
`getMaximumConstraintError`()	Accessor to maximum allowed constraint error.
`getMaximumEvaluationNumber`()	Accessor to maximum allowed number of evaluations.
`getMaximumIterationNumber`()	Accessor to maximum allowed number of iterations.
`getMaximumRelativeError`()	Accessor to maximum allowed relative error.
`getMaximumResidualError`()	Accessor to maximum allowed residual error.
`getName`()	Accessor to the object's name.
`getProblem`()	Accessor to optimization problem.
`getResult`()	Accessor to optimization result.
`getShadowedId`()	Accessor to the object's shadowed id.
`getStartingPoint`()	Accessor to starting point.
`getVisibility`()	Accessor to the object's visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`run`()	Launch the optimization.
`setAlgorithmName`(algoName)	Accessor to algoName parameter.
`setMaximumAbsoluteError`(maximumAbsoluteError)	Accessor to maximum allowed absolute error.
`setMaximumConstraintError`(maximumConstraintError)	Accessor to maximum allowed constraint error.
`setMaximumEvaluationNumber`(...)	Accessor to maximum allowed number of evaluations.
`setMaximumIterationNumber`(maximumIterationNumber)	Accessor to maximum allowed number of iterations.
`setMaximumRelativeError`(maximumRelativeError)	Accessor to maximum allowed relative error.
`setMaximumResidualError`(maximumResidualError)	Accessor to maximum allowed residual error.
`setName`(name)	Accessor to the object's name.
`setProblem`(problem)	Accessor to optimization problem.
`setProgressCallback`(*args)	Set up a progress callback.
`setResult`(result)	Accessor to optimization result.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setStartingPoint`(startingPoint)	Accessor to starting point.
`setStopCallback`(*args)	Set up a stop callback.
`setVisibility`(visible)	Accessor to the object's visibility state.

getVerbose
setVerbose

__init__(*args)¶

static GetAlgorithmNames()¶

Retrieves the names of the available optimization algorithms.

Returns:

algoNameDescription: The names of the available optimization algorithms.

getAlgorithmName()¶

Accessor to algoName parameter. Retrieves the name of the optimization algorithm used.

Returns:

algoNamestr: The name of the optimization algorithm used.

getClassName()¶

Accessor to the object’s name.

Returns:

class_namestr: The object class name (object.__class__.__name__).

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getMaximumAbsoluteError()¶

Accessor to maximum allowed absolute error.

Returns:

maximumAbsoluteErrorfloat: Maximum allowed absolute error, where the absolute error is defined by $\epsilon^a_n=\|\vect{x}_{n+1}-\vect{x}_n\|_{\infty}$ where $\vect{x}_{n+1}$ and $\vect{x}_n$ are two consecutive approximations of the optimum.

getMaximumConstraintError()¶

Accessor to maximum allowed constraint error.

Returns:

maximumConstraintErrorfloat: Maximum allowed constraint error, where the constraint error is defined by $\gamma_n=\|g(\vect{x}_n)\|_{\infty}$ where $\vect{x}_n$ is the current approximation of the optimum and $g$ is the function that gathers all the equality and inequality constraints (violated values only)

getMaximumEvaluationNumber()¶

Accessor to maximum allowed number of evaluations.

Returns:

Nint: Maximum allowed number of evaluations.

getMaximumIterationNumber()¶

Accessor to maximum allowed number of iterations.

Returns:

Nint: Maximum allowed number of iterations.

getMaximumRelativeError()¶

Accessor to maximum allowed relative error.

Returns:

maximumRelativeErrorfloat: Maximum allowed relative error, where the relative error is defined by $\epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty}$ if $\|\vect{x}_{n+1}\|_{\infty}\neq 0$ , else $\epsilon^r_n=-1$ .

getMaximumResidualError()¶

Accessor to maximum allowed residual error.

Returns:

maximumResidualErrorfloat: Maximum allowed residual error, where the residual error is defined by $\epsilon^r_n=\frac{\|f(\vect{x}_{n+1})-f(\vect{x}_{n})\|}{\|f(\vect{x}_{n+1})\|}$ if $\|f(\vect{x}_{n+1})\|\neq 0$ , else $\epsilon^r_n=-1$ .

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getProblem()¶

Accessor to optimization problem.

Returns:

problemOptimizationProblem: Optimization problem.

getResult()¶

Accessor to optimization result.

Returns:

resultOptimizationResult: Result class.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

idint: Internal unique identifier.

getStartingPoint()¶

Accessor to starting point.

Returns:

startingPointPoint: Starting point.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

hasVisibleNamebool: True if the name is not empty and not the default one.

run()¶: Launch the optimization.

setAlgorithmName(algoName)¶

Accessor to algoName parameter. Sets the optimization algorithm to use. Possible values for algoName are B-BB, B-OA, B-QG, B-Hyb, B-Ecp, B-iFP. See Bonmin’s online documentation for more details. Default is B-BB.

Parameters:

algoNamestr: The name of the optimization algorithm to use.

setMaximumAbsoluteError(maximumAbsoluteError)¶

Accessor to maximum allowed absolute error.

Parameters:

maximumAbsoluteErrorfloat: Maximum allowed absolute error, where the absolute error is defined by $\epsilon^a_n=\|\vect{x}_{n+1}-\vect{x}_n\|_{\infty}$ where $\vect{x}_{n+1}$ and $\vect{x}_n$ are two consecutive approximations of the optimum.

setMaximumConstraintError(maximumConstraintError)¶

Accessor to maximum allowed constraint error.

Parameters:

maximumConstraintErrorfloat: Maximum allowed constraint error, where the constraint error is defined by $\gamma_n=\|g(\vect{x}_n)\|_{\infty}$ where $\vect{x}_n$ is the current approximation of the optimum and $g$ is the function that gathers all the equality and inequality constraints (violated values only)

setMaximumEvaluationNumber(maximumEvaluationNumber)¶

Accessor to maximum allowed number of evaluations.

Parameters:

Nint: Maximum allowed number of evaluations.

setMaximumIterationNumber(maximumIterationNumber)¶

Accessor to maximum allowed number of iterations.

Parameters:

Nint: Maximum allowed number of iterations.

setMaximumRelativeError(maximumRelativeError)¶

Accessor to maximum allowed relative error.

Parameters:

maximumRelativeErrorfloat: Maximum allowed relative error, where the relative error is defined by $\epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty}$ if $\|\vect{x}_{n+1}\|_{\infty}\neq 0$ , else $\epsilon^r_n=-1$ .

setMaximumResidualError(maximumResidualError)¶

Accessor to maximum allowed residual error.

Parameters:

maximumResidualErrorfloat: Maximum allowed residual error, where the residual error is defined by $\epsilon^r_n=\frac{\|f(\vect{x}_{n+1})-f(\vect{x}_{n})\|}{\|f(\vect{x}_{n+1})\|}$ if $\|f(\vect{x}_{n+1})\|\neq 0$ , else $\epsilon^r_n=-1$ .

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setProblem(problem)¶

Accessor to optimization problem.

Parameters:

problemOptimizationProblem: Optimization problem.

setProgressCallback(*args)¶

Set up a progress callback.

Can be used to programmatically report the progress of an optimization.

Parameters:

callbackcallable: Takes a float as argument as percentage of progress.

Examples

>>> import sys
>>> import openturns as ot
>>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2'])
>>> problem = ot.OptimizationProblem(rosenbrock)
>>> solver = ot.OptimizationAlgorithm(problem)
>>> solver.setStartingPoint([0, 0])
>>> solver.setMaximumResidualError(1.e-3)
>>> solver.setMaximumEvaluationNumber(10000)
>>> def report_progress(progress):
...     sys.stderr.write('-- progress=' + str(progress) + '%\n')
>>> solver.setProgressCallback(report_progress)
>>> solver.run()

setResult(result)¶

Accessor to optimization result.

Parameters:

resultOptimizationResult: Result class.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

idint: Internal unique identifier.

setStartingPoint(startingPoint)¶

Accessor to starting point.

Parameters:

startingPointPoint: Starting point.

setStopCallback(*args)¶

Set up a stop callback.

Can be used to programmatically stop an optimization.

Parameters:

callbackcallable: Returns an int deciding whether to stop or continue.

Examples

>>> import openturns as ot
>>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2'])
>>> problem = ot.OptimizationProblem(rosenbrock)
>>> solver = ot.OptimizationAlgorithm(problem)
>>> solver.setStartingPoint([0, 0])
>>> solver.setMaximumResidualError(1.e-3)
>>> solver.setMaximumEvaluationNumber(10000)
>>> def ask_stop():
...     return True
>>> solver.setStopCallback(ask_stop)
>>> solver.run()

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visiblebool: Visibility flag.

Examples using the class¶

Optimization using bonmin

OpenTURNS

An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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Bonmin¶

Examples using the class¶

Algorithm	Description
B-BB	NLP-based branch-and-bound
B-OA	Outer-approximation decomposition
B-QG	Quesada and Grossmann branch-and-cut
B-Hyb	Hybrid outer-approximation based branch-and-cut
B-Ecp	ECP cuts based branch-and-cut a la FilMINT
B-iFP	Iterated feasibility pump algorithm for MINLP